{"list":"other","total":171,"rows":[{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12049},{"id":"1807.03748","title":"Representation Learning with Contrastive Predictive Coding","created":"2018-07-10","cited_by_count":7777},{"id":"1710.09412","title":"mixup: Beyond Empirical Risk Minimization","created":"2017-10-25","cited_by_count":5183},{"id":"2006.07733","title":"Bootstrap your own latent: A new approach to self-supervised Learning","created":"2020-06-13","cited_by_count":4750},{"id":"1506.02142","title":"Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning","created":"2015-06-06","cited_by_count":4448},{"id":"1512.04150","title":"Learning Deep Features for Discriminative Localization","created":"2015-12-14","cited_by_count":2436},{"id":"1704.00028","title":"Improved Training of Wasserstein GANs","created":"2017-03-31","cited_by_count":2235},{"id":"1905.02249","title":"MixMatch: A Holistic Approach to Semi-Supervised Learning","created":"2019-05-06","cited_by_count":1391},{"id":"1802.04712","title":"Attention-based Deep Multiple Instance Learning","created":"2018-02-13","cited_by_count":1095},{"id":"1610.02242","title":"Temporal Ensembling for Semi-Supervised Learning","created":"2016-10-07","cited_by_count":1057},{"id":"1705.02315","title":"ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases","created":"2017-05-05","cited_by_count":972},{"id":"1703.02910","title":"Deep Bayesian Active Learning with Image Data","created":"2017-03-08","cited_by_count":819},{"id":"2103.02503","title":"Domain Generalization: A Survey","created":"2021-03-03","cited_by_count":764},{"id":"2004.09666","title":"Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images","created":"2020-04-20","cited_by_count":745},{"id":"1902.06162","title":"Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey","created":"2019-02-16","cited_by_count":657},{"id":"2003.08119","title":"The Future of Digital Health with Federated Learning","created":"2020-03-18","cited_by_count":633},{"id":"2106.00908","title":"TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification","created":"2021-06-02","cited_by_count":555},{"id":"1710.05006","title":"Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)","created":"2017-10-13","cited_by_count":549},{"id":"2112.07804","title":"Tackling the Generative Learning Trilemma with Denoising Diffusion GANs","created":"2021-12-15","cited_by_count":516},{"id":"2112.03126","title":"Label-Efficient Semantic Segmentation with Diffusion Models","created":"2021-12-06","cited_by_count":499},{"id":"2011.08939","title":"Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning","created":"2020-11-17","cited_by_count":478},{"id":"1606.04586","title":"Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning","created":"2016-06-14","cited_by_count":441},{"id":"1907.07034","title":"Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation","created":"2019-07-16","cited_by_count":379},{"id":"1902.09063","title":"A large annotated medical image dataset for the development and evaluation of segmentation algorithms","created":"2019-02-25","cited_by_count":373},{"id":"2206.02647","title":"Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning","created":"2022-06-06","cited_by_count":359},{"id":"2101.05224","title":"Big Self-Supervised Models Advance Medical Image Classification","created":"2021-01-13","cited_by_count":320},{"id":"2001.06535","title":"CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation","created":"2020-01-17","cited_by_count":309},{"id":"2003.13216","title":"Learning to Learn Single Domain Generalization","created":"2020-03-30","cited_by_count":296},{"id":"2103.06030","title":"FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space","created":"2021-03-10","cited_by_count":287},{"id":"1908.10454","title":"Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation","created":"2019-08-27","cited_by_count":260},{"id":"2203.12081","title":"DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification","created":"2022-03-22","cited_by_count":256},{"id":"2103.00550","title":"A Survey on Deep Semi-supervised Learning","created":"2021-02-28","cited_by_count":251},{"id":"1910.03667","title":"REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs","created":"2019-10-08","cited_by_count":228},{"id":"2301.00785","title":"CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection","created":"2023-01-02","cited_by_count":224},{"id":"2009.04448","title":"Semi-supervised Medical Image Segmentation through Dual-task Consistency","created":"2020-09-09","cited_by_count":216},{"id":"1605.01397","title":"Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)","created":"2016-05-04","cited_by_count":198},{"id":"2004.14133","title":"Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images","created":"2020-04-22","cited_by_count":184},{"id":"1804.06353","title":"Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis","created":"2018-04-17","cited_by_count":179},{"id":"2009.11169","title":"Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks","created":"2020-09-23","cited_by_count":178},{"id":"1506.06448","title":"DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation","created":"2015-06-22","cited_by_count":159},{"id":"1704.07520","title":"Stein Variational Gradient Descent as Gradient Flow","created":"2017-04-25","cited_by_count":152},{"id":"2007.10732","title":"Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images","created":"2020-07-21","cited_by_count":152},{"id":"1910.02923","title":"A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis","created":"2019-10-07","cited_by_count":151},{"id":"1706.04737","title":"Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation","created":"2017-06-15","cited_by_count":146},{"id":"2104.05833","title":"Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization","created":"2021-04-12","cited_by_count":141},{"id":"1903.00348","title":"Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation","created":"2019-02-28","cited_by_count":140},{"id":"2006.03829","title":"3D Self-Supervised Methods for Medical Imaging","created":"2020-06-06","cited_by_count":134},{"id":"1808.04277","title":"BACH: Grand Challenge on Breast Cancer Histology Images","created":"2018-08-13","cited_by_count":131},{"id":"1901.11448","title":"Feature-Critic Networks for Heterogeneous Domain Generalization","created":"2019-01-31","cited_by_count":130},{"id":"1808.01097","title":"CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images","created":"2018-08-03","cited_by_count":126},{"id":"1706.02262","title":"InfoVAE: Information Maximizing Variational Autoencoders","created":"2017-06-07","cited_by_count":124},{"id":"2009.12829","title":"Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization","created":"2020-09-27","cited_by_count":123},{"id":"2001.05647","title":"Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results","created":"2020-01-16","cited_by_count":122},{"id":"2006.05278","title":"An Overview of Deep Semi-Supervised Learning","created":"2020-06-09","cited_by_count":121},{"id":"1710.04043","title":"Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning","created":"2017-10-11","cited_by_count":111},{"id":"2109.09960","title":"Mutual Consistency Learning for Semi-supervised Medical Image Segmentation","created":"2021-09-21","cited_by_count":108},{"id":"1712.04086","title":"PacGAN: The power of two samples in generative adversarial networks","created":"2017-12-12","cited_by_count":104},{"id":"1606.01583","title":"Semi-Supervised Learning with Generative Adversarial Networks","created":"2016-06-05","cited_by_count":103},{"id":"2012.07042","title":"Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency","created":"2020-12-13","cited_by_count":102},{"id":"2112.04894","title":"Semi-Supervised Medical Image Segmentation via Cross Teaching between CNN and Transformer","created":"2021-12-09","cited_by_count":100},{"id":"2008.05571","title":"Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotations","created":"2020-08-12","cited_by_count":89},{"id":"2108.06227","title":"SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation","created":"2021-08-13","cited_by_count":86},{"id":"1604.02677","title":"DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation","created":"2016-04-10","cited_by_count":84},{"id":"2001.01599","title":"Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Images","created":"2020-01-06","cited_by_count":81},{"id":"2006.16806","title":"Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation","created":"2020-06-28","cited_by_count":81},{"id":"2111.03452","title":"Generalized Radiograph Representation Learning via Cross-supervision between Images and Free-text Radiology Reports","created":"2021-11-04","cited_by_count":80},{"id":"1605.07866","title":"DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks","created":"2016-05-25","cited_by_count":79},{"id":"1910.02241","title":"Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik's Cube","created":"2019-10-05","cited_by_count":76},{"id":"2301.13155","title":"Advancing Radiograph Representation Learning with Masked Record Modeling","created":"2023-01-30","cited_by_count":75},{"id":"1806.05473","title":"Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network","created":"2018-06-14","cited_by_count":74},{"id":"1810.13230","title":"Methods for Segmentation and Classification of Digital Microscopy Tissue Images","created":"2018-10-31","cited_by_count":73},{"id":"1908.10555","title":"CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation","created":"2019-08-28","cited_by_count":73},{"id":"1911.01218","title":"Semi-Supervised Medical Image Segmentation via Learning Consistency under Transformations","created":"2019-11-04","cited_by_count":71},{"id":"1907.02757","title":"Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction","created":"2019-07-05","cited_by_count":70},{"id":"2012.02383","title":"SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images","created":"2020-12-04","cited_by_count":67},{"id":"2203.01475","title":"CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision","created":"2022-03-03","cited_by_count":64},{"id":"1807.08284","title":"Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge","created":"2018-07-22","cited_by_count":61},{"id":"2110.08762","title":"Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation","created":"2021-10-17","cited_by_count":61},{"id":"2303.11568","title":"Large AI Models in Health Informatics: Applications, Challenges, and the Future","created":"2023-03-21","cited_by_count":61},{"id":"2109.04379","title":"Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts","created":"2021-09-09","cited_by_count":60},{"id":"2005.07377","title":"Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model","created":"2020-05-15","cited_by_count":59},{"id":"2206.01794","title":"Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology","created":"2022-06-03","cited_by_count":58},{"id":"1912.05396","title":"Multimodal Self-Supervised Learning for Medical Image Analysis","created":"2019-12-11","cited_by_count":57},{"id":"2007.05448","title":"Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images","created":"2020-07-10","cited_by_count":55},{"id":"2112.09356","title":"UniMiSS: Universal Medical Self-Supervised Learning via Breaking Dimensionality Barrier","created":"2021-12-17","cited_by_count":55},{"id":"2009.10190","title":"Federated Learning for Computational Pathology on Gigapixel Whole Slide Images","created":"2020-09-21","cited_by_count":54},{"id":"2203.02533","title":"BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotation","created":"2022-03-04","cited_by_count":54},{"id":"2003.09108","title":"FocalMix: Semi-Supervised Learning for 3D Medical Image Detection","created":"2020-03-20","cited_by_count":52},{"id":"2006.12890","title":"Scribble2Label: Scribble-Supervised Cell Segmentation via Self-Generating Pseudo-Labels with Consistency","created":"2020-06-23","cited_by_count":52},{"id":"2109.07407","title":"Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation","created":"2021-09-15","cited_by_count":52},{"id":"1701.00794","title":"Constrained Deep Weak Supervision for Histopathology Image Segmentation","created":"2017-01-03","cited_by_count":51},{"id":"1807.04668","title":"Learning to Segment Medical Images with Scribble-Supervision Alone","created":"2018-07-12","cited_by_count":48},{"id":"2204.10437","title":"DiRA: Discriminative, Restorative, and Adversarial Learning for Self-supervised Medical Image Analysis","created":"2022-04-21","cited_by_count":48},{"id":"2007.11067","title":"Self-supervised Feature Learning via Exploiting Multi-modal Data for Retinal Disease Diagnosis","created":"2020-07-21","cited_by_count":47},{"id":"1808.03887","title":"Semi-supervised Skin Lesion Segmentation via Transformation Consistent Self-ensembling Model","created":"2018-08-12","cited_by_count":45},{"id":"2101.02323","title":"Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation","created":"2021-01-07","cited_by_count":45},{"id":"2010.09298","title":"Double-Uncertainty Weighted Method for Semi-supervised Learning","created":"2020-10-19","cited_by_count":44},{"id":"2002.07613","title":"An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization","created":"2020-02-13","cited_by_count":43},{"id":"2112.09645","title":"Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation","created":"2021-12-17","cited_by_count":43}],"next_cursor":"100"}